I. Technical Field
The present invention relates to multi-microphone source tracking and noise suppression in acoustic environments.
II. Background Art
A number of different speech and audio signal processing algorithms are currently used in cellular communication systems. For example, conventional cellular telephones implement standard speech processing algorithms such as acoustic echo cancellation, multi-microphone noise reduction, single-channel suppression, packet loss concealment, and the like, to improve speech quality. It is often beneficial for systems, such as cellular handsets with multiple microphones and speakerphone capabilities, to apply noise suppression to provide an enhanced speech signal for speech communication.
The use of speech processing applications on portable devices requires robustness to acoustic environments. It is often beneficial for such systems to apply noise suppression to provide an enhanced speech signal for speech communication. Acoustic scene analysis (ASA) is used for multi-microphone noise reduction (MMNR) and/or suppression, because it allows decisions to be made regarding the location and activity of the desired source. For multi-microphone noise suppression, the angle of incidence of the desired source (DS) is determined in order to appropriately steer a beamformer to the DS so as to better capture sound from the DS. Additionally, durations of DS activity/inactivity must be recognized in order to appropriately update statistical parameters of the system.
Traditional ASA methods utilize spatial information such as time difference of arrival (TDOA) or energy levels to locate acoustic sources. The DS location can be estimated by comparing observed measures to those expected for DS behavior. For example, a DS can be expected to show a spatial signature similar to a point source, with high energy relative to interfering sources. A major drawback to such ASA methods is that multiple acoustic sources may be present which behave similarly to the expected signature. In such scenarios the DS cannot be accurately differentiated from interfering sources.